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Time Series Analysis with Python Cookbook

You're reading from   Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

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Product type Paperback
Published in Apr 2025
Publisher
ISBN-13 9781805124283
Length 98 pages
Edition 2nd Edition
Languages
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Author (1):
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Tarek A. Atwan Tarek A. Atwan
Author Profile Icon Tarek A. Atwan
Tarek A. Atwan
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Table of Contents (13) Chapters Close

1. Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation FREE CHAPTER
2. Getting Started with Time Series Analysis 3. Reading Time Series Data from Files 4. Reading Time Series Data from Databases 5. Persisting Time Series Data to Files 6. Persisting Time Series Data to Databases 7. Working with Date and Time in Python 8. Handling Missing Data 9. Outlier Detection Using Statistical Methods 10. Exploratory Data Analysis and Diagnosis 11. Building Univariate Time Series Models Using Statistical Methods 12. Additional Statistical Modeling Techniques for Time Series 13. Outlier Detection Using Unsupervised Machine Learning

Technical requirements

In this chapter and going forward, we will extensively use pandas 2.1.3 (released on November 10, 2023. This applies to all the recipes in this chapter.

Load these libraries in advance, since you will be using them throughout the chapter:

import pandas as pd
import numpy as np
import datetime as dt

You will use dt, np, and pd aliases going forward.

You can download the Jupyter notebooks from the GitHub repository at https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook./tree/main/code/Ch6 to follow along.

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